Evaluating the impact of digitalization, renewable energy use, and technological innovation on load capacity factor in G8 nations

Ecosystems are in danger due to human-caused air, water, and soil pollution, so it is important to find the underlying causes of this issue and develop practical solutions. This study adds to environmental research gap by suggesting the load capability factor (LCF) and using it to look at the factors affectting environmental health. The load capacity factor simplifies monitoring environmental health by illustrating the distinction between ecological footprint and biocapacity. We examine the interplay between mobile phone users (Digitalization DIG), technological advancements (TEC), renewable energy use, economic growth, and financial development. This study assesses G8 economies’ data from 1990 to 2018, using a Cross-Section Improved Autoregressive Distributed Lag CS-ARDL estimator and a cointegration test. The data shows that green energy, TEC innovation, and DIG are all beneficial for natural health. Based on the results of this study, the G8 governments should focus on environmental policies that promote economic growth, increase the use of renewable energy sources, guide technological progress in key areas, and encourage the development of digital information and communications technologies that are better for the environment.

www.nature.com/scientificreports/ decline between 1990 and 2018, the LCF levels in Germany, France, and the United States are still well below "1". It is, therefore, essential to look into the environmental issues faced and solutions implemented by the G8 nations 20 . As a continuation, this research focuses on the G8 countries for the following reasons: G8 nations have expand its economies by creating the way for rapid advances in commerce and industry. Still, their efforts to do so come at the expense of environmental quality 27 ; secondly, because environmental deterioration is so severe, officials in the G8 countries must look into the causes of environmental degradation; however, previous empirical work has been largely focused on ecological destruction measures resulting from human consumption such as EF 28,29 . The EF collects data on air, water, and soil pollution, but the extent to which they deplete these supplies needs to be made clear. This research helps bridge the gap by proposing a novel ecological economics hypothesis 30 . The cross-sectional dependency in the data, the CS-ARDL, and Durbin-Hausmann cointegration techniques are applied (CSD). Given the higher potential for the G8 nations to influence one another, the findings of CSD research may be instructive. Figure 1 shows the LCF of G8 countries during 1990-2018. This study contributes to knowledge gap about LCF (load capacity factor) in G-8 countries in light of technological advancement and renewable energy in the scenario of globalization. Preceding this study, some studies found that new technology, digitalization, and renewable energy impact LCF. Many studies have been done on LCF and other related variables in other countries or regions. This study contributes originality in the case of the G-8 economies.
This research has insightful data from various perspectives. This is how the investigation is presented: the literature on the effect of (EF) research and development, DIG, and renewable energy on the environment is discussed in Section "Review of the literature". Studies investigating what factors influence the LCF are also discussed here. We described the data used and the research techniques employed in Section "Data and methodology". Empirical findings are given and discussed in Section "Results and discussion". The report concludes with several suggestions for future policy in Section "Conclusion and policy implications".

Review of the literature
There are four subsections in this section. The first part of this paper focuses on the connection between renewable energy and the natural world. In contrast, the subsequent parts examine the relationships between (TA) spending and the natural world. A synthesis of research into the LCF is provided, and research gaps are highlighted in the final section.
The correlation between RE and the environment. From an environmental perspective, several studies have shown that renewable energy sources are preferable to fossil fuel. Two studies 31,32 , indicated that RE reduces EF in comparable high-polluting countries. The results of this research, however, were different from one another. However, as Ref. 9 pointed out, the pace at which CO 2 emissions rise while switching to renewable energy is only half that when switching to fossil fuels. In contrast, studies undertaken in Turkey 33 , and China 19 , concluded that the use of renewable sources of energy does not significantly reduce CO 2 , EF, or LCF and that there is no consumption of renewable resources responsibly or efficiently 10 . In a study by Mehmood et al. 34  www.nature.com/scientificreports/ mated the impact of the transition to green energy, economic growth, and natural resource availability on carbon dioxide emissions in the context of evolving political, economic, and financial risks. The findings indicate that using natural resource rents is positively associated with CO 2 emissions, whereas adopting green energy sources is negatively correlated with CO 2 emissions. Mitigating political and financial risks can lead to reduced carbon dioxide emissions and improved environmental conditions. Furthermore, it has been observed that the restriction of CO 2 emissions occurs after a certain threshold of economic growth has been achieved, thereby validating the existence of an inverted U-shaped relationship between economic growth and CO 2 . Hence, recognizing the necessity for a more discerning assessment of the complexities entailed in investigating the ecological footprint, this research scrutinizes the interconnections among natural resources, technological advancements, financial expansion, and the consequent ecological footprint in developing nations 35 . The relationship between TEC spending and the environment. TEC costs cut the EF in all 28 EU member states, according to Ref. 36 . The authors of Ref. 37 found that supporting research and development reduces carbon dioxide emissions using CCEMG and AMG panel data estimators. Similarly, Ref. 38 utilized Tobit and probity models to evaluate Chinese firm data and discovered that increasing TEC spending would enhance green innovation efforts and success. Fareed et al. (2022) found in panel quantile regression research of Ref. 38 European Union states that expenditure on TEC minimizes the bad impacts of economic inclusion on environmental factors (EF), reducing or postponing ecological deprivation. An interactive fixed effect approach study of 27 nations undertaken by Ref. 39 found that expenditure on renewable energy technological advancement (TEC) correlates with decreased (CH 4 ) methane, (CO), (NO), and (CO 2 ) carbon dioxide emissions 40 , using the Degree of Operating Leverage analysis, increasing investment in TEC reduced CO 2 emissions in Saudi Arabia. Using Westerlund cointegration and multiple panel estimators, Ref. 4 determined that TEC investment considerably and positively contributed to cutting GHG emissions across 40 nations 41 . Numerous reports have shown the environmental benefits of investing in research and development. Contrary to popular opinion, TEC investment causes environmental damage in 96 nations, as Ref. 42 show using an inclusive nested spatial model. In a similar vein, Ref. 43 found that TEC investment increases (EF) and pollution in five developing countries using the Panel Generalized Method of Moments and the Fully Modified Ordinary Least Squares.
Relationship between DIG and the environment. Due to technology significance to progress, research into the impact of DIGs on pollution is ongoing. At the same time, the green benefit of DIGs is generally modest in developing nations; Ref. 44 Gaussian Mixture Models examinations of 60 countries indicated that DIG adaptation in metropolitan zones helps cut CO 2 emissions. Using the panel Vector Autoregressive Model methodology, Ref. 45 discovered that DIG helps with CO 2 reduction in 18 African countries by allowing for greater access to resources and carbon monitoring 46 . Finds that widespread usage of DIG has helped Chinese cities lower carbon intensity after considering geographical and temporal consequences. Using the (CUP-FM) estimator 47 , discovered that DIG reduces (CO 2 ) emissions in 17 countries in Asia. According to the authors, policymakers should prioritize environmental protection when formulating new legislation, and DIG-enabled economic policies should play a pivotal part in this effort. The influence of (DIG) on organizational effectiveness (EF) has been the focus of numerous studies utilizing various econometric approaches and used the ARDL panel to determine that DIG lessens (EF) in the world's top ten polluting countries. Kahouli et al. 48 used Johansen cointegration and (VECM) to discover that DIG reduces (EF) in Saudi Arabia. A study using the AMG estimator 49 found that internet usage was associated with decreased EF across all G8 countries. Studies consistently show that information and communication technologies are harmful to the environment. The G8 has been demonstrated to have lower CO 2 emissions due to the usage of DIG; however, is found the reverse to be true when using PMG. Analysis of data from the N11 nations conducted by 50  LCF empirical research. Empirical research has usually concentrated on the part that CO 2 emissions and the EF play in these problems; however, much research has been done on the causes of climate change. The ARDL approach, modified for the United States and Japan, allowed the simultaneous investigation of biocapacity and EF to investigate the variables that influence LCF. This groundbreaking study showed that LCF is restrained by financial resources in the United States. Used the dynamic ARDL approach to determine that in China, energy intensity, resource rent, and income negatively influence LCF, but human capital has a favorable effect. Specialists have studied the factors that impact LCF in the subject. The authors of 53 argue that although applying the (ARDL) method to India and income does reduce LCF, all contribute to better environmental results. Awosusi et al. 54 used ARDL for Brazil and found that globalization increases environmental quality, but urbanization has little effect on LCF. The literature review is divided into four parts, each addressing a different area of study that needs addressing. First, research on the causes of LCF in the Group of Seven needs to be completed.
Furthermore, there needs to be more studies examining how DIG affects LCF. Does information and communication technology affect biological potential? Do increased Internet access, mobile phone use, and other IT developments positively or negatively affect the world's natural resource supply? By addressing these issues, we may contribute new knowledge and close gaps in the existing body of research 55 .
In addition, developments in the building industry in G20 countries worsen environmental quality. Finally, earlier studies show that technological progress has only sometimes had beneficial effects on environmental quality. Empirical studies have shown conflicting results. Thus, additional research into its impact on environmental www.nature.com/scientificreports/ quality is required. There is also a need for more information on how BRICS-T country green tech innovations have affected ecological quality. Therefore, the BRICS-T nations are the primary focus of this study. This research aims to shed light on this problem and give a thorough environmental evaluation of OECD nations by analyzing the impact of human capital, income, natural resources, urbanization, and renewable energy on the load capacity factor for 26 OECD countries between 1980 and 2018. This research tests the load capacity curve (LCC) hypothesis using the recently established quantile common correlated effects mean group (QMG) estimator. Our findings provide credence to the LCC hypothesis and support a U-shaped relationship between wealth and environmental quality. Human capital, resource rent, and renewable energy all boost the load capacity factor, according to the QMG estimator, but urbanization hurts ecological quality. Table 1 shows the data symbols and origins used for the study.
In Table 1, the data for Load capacity factor (LCF) has been obtained from Global Footwork Network (GFN). The data for Digitalization has been derived from GINI index, while data for Natural Resources and Economic growth has been derived from World Data Indicators, (WDI). The data for Government stability has been derived from International country risk guide, (ICRG).

Data and methodology
Before running a unit root test, it's important to rule out the possibility of cross-sectional dependence (CD). CD shares traits with economic integration, residual reliance, and shared stocks 60 . Neglecting (CD) can lead to skewed statistics, misaligned proportions, prejudiced stationarity, and skewed cointegration 61 . We used the Chudik and Pesaran 62 CD test to identify potential problems. Once results from CD have been obtained, the panel data is examined with a unit root or stationarity technique.
Cointegration testing. Next, after utilizing the unit root test, we apply an improved type of 63 to check for slope homogeneity and heterogeneity. In CD, the attributes of size distortion in panel data cannot be predicted by the first generation of cointegration tests established and used previously 64 . None of the researchers took CD in a cross-sectional study into account. The approach developed versions for slope, CD, and correlated error variation.

CS-ARDL.
This research used the CS-ARDL method to calculate the short-and long-term coefficients. As previously mentioned 65 , CS-ARDL employs a DCCI (dynamic common correlated impact) predictor to address the issues of heterogeneity and cross-section dependence. Equation (1) is the mathematical representation of CS-ARDL.
In the presence of CD, using Eq. (1), based on the autoregressive distributed lag (ARDL) model, will lead to indeterminate results. Regressor variables are averaged over cross-sections to alter Eq. (2). As a result, and we can rest any doubts about the existence of the CD-induced threshold effect 62 .

whereas,
Existing delays among all parameters are denoted by aw, as, and axe. The independent variables are represented by Z (i.t), while the dependent variable, H, is the per capita carbon emission based on consumption. The average cross-sectional value (X) is used instead of considering trends to counteract the spillover mentioned above. (1)

Results and discussion
First, we check for (CSD), between countries. Cross-sectional dependence (CSD) measures how one country's economic fortunes affect those of another. Traditional panel data estimators may need more reliability and efficiency if independent variables cause the CSD. Parameter inconsistency is another result of CSD 66 . Additionally, series with CSD fail conventional unit root testing 67 . To begin, we employ the CD test of 68 and the (LM) test of (Breusch and Pagan) to examine whether or not CSD exists. Table 2 presents the outcome of the (CSD) analysis. We found evidence against the absence of CSD, as indicated by the CD and LM tests. The outcomes of the Pesaran CIPS unit root test 69 are shown in Table 3. No discernible pattern in GDP or DIG growth was found (0). The (LCF), (RE), and TEC all have a unit root, but the initial disparities between them and the mean are stationary (1). Assuming that the independent variables are integrated at distinct orders (I (0) and I (1)), here we also discover that the dependent variable is blended at I (1). Since the Westerlund cointegration test (see Table 4) permits the investigation of long-run correlations between variables of varying integration orders, we use it. Adding CSD to the display is another advantage of this test.
In the next stage, we evaluate the short-and long-run determinants of the (LCF) using the (CS-ARDL) approach. Table 5 demonstrates the results of the (CS-ARDL) approach.
The CS-ARDL estimator concludes that RE, DIG, TEC, and FD contribute to a better environment over time. A 1% rise in renewable energy sources and a 1% increase in TEC are both found to boost LCF by 0.078% and 0.046%, respectively. While DIG also boosts LCF by 0.033% and FD by 0.028% in G8 countries. On the other hand, GDP in G8 nations is negative by -0.014% and does not support LCF here. According to data collected from WDI (World Data Indicators), Ref. 11 research on the United States and Japan 19 and research on the G8 countries both find that renewable energy plays an important influence in Ecological Footprints. The advanced technologies and robust renewable energy infrastructures of the G8 nations help to protect the environment.
In contrast to Ref. 22 , we find that TEC has a positive environmental impact in the G8 countries, as 49 also notes. Incorporating TEC's adaptability and environmentally friendly manufacturing technologies can lessen the environmental burden and boost the production of useful biomaterials. Therefore, the G8 nations can boost their environmental protection by bolstering their TEC systems and spreading green TEC practices. www.nature.com/scientificreports/ The LCF is positively correlated with TEC spending, with a 1% rise in TEC spending leading to a 0.046 percentage point increase in the LCF. In contrast to their long-term benefits, the immediate advantages of green energy and TEC are greater 70 , found the same thing for the G8 countries, so this conclusion is consistent with their research. Increased investment in TEC by the G8 governments can greatly increase the LCF by stimulating the creation of innovative clean energy technologies, raising public consciousness about the importance of these issues, and improving energy efficiency. In contrast, FD is positively important by 0.028%. As a result, FD is considered to be ecologically benign in G8 nations.
This result also holds at the 5% significance level in the near run. The findings show that the ECM is negative, less than 1, and significantly statistically different from zero. Short-run imbalances are typically adjusted in 3.3 years, according to the ECM coefficient of 0.451. Table 6 presents the robustness check.
To check the authenticity of the findings, this work applies AMG and FMOLS tests. These tests efficiently analyze the panel data and can accommodate the CD in the data. The findings show that RE, DIG, TEC, and FD are ecologically supportive, but economic growth degrades the environment. These results endorse the findings of CS-ARDL.

Conclusion and policy implications
Several anthropogenic environmental problems pose hazards to human health and a country's economy. Many scholars have used the environmental Kuznets curve to examine the causes of these issues. The environmental Kuznets curve employs indicators such as carbon dioxide emissions and ecological footprints, but more is needed to capture the complete scope of environmental problems. By utilizing cutting-edge second-generation panel data methods for the G8 countries, this research focuses on the load capacity factor within this context. This research seeks new insight into environmental economics by examining the effect of technological advancement, renewable energy, and digitalization on the load capacity factor. Our study offers crucial insight for G8 officials interested in enhancing environmental quality.
The cross sectional autoregressive distributed lag results demonstrate that green energy sources, government investment in technological advancement, and the adoption of cutting-edge information and communication technologies boost load capacity factor over time. So environmental strategies considering technological advancement, renewable energy, digitalization, and financial development are necessary to enhance environmental quality. As a first point, there is a noticeable link between financial development and load capacity factor (LCF). Growth in these economies leads to environmental problems like the overuse of natural fuels and other resources.
To ensure environmental quality and protect nature, the governments of the four countries need additional policies and measures beyond economic growth. Second, the positive impact renewable energy has on the planet demonstrates the need for the G8 countries to increase their use of solar, wind, and biomass power and the proportion of their total energy usage from these renewable resources. Third, the savings from implementing  www.nature.com/scientificreports/ digitalization in sectors like transportation, power, and communications free up capital for other environmentally beneficial projects. Using smart digitalization tools, the G8 nations can modernize their industrial structure in an ecologically responsible and efficient way regarding energy use. Green digitalization should also be incorporated into new forms of logistics service, carbon reduction technologies, and energy-saving procedures in the G8 nations. The load capacity factor can benefit greatly from all of these, and they can all help it grow. As a result, the G8 countries should prioritize technological advancement funding as a key component of their environmental policies and encourage the creation of more sustainable methods of manufacturing. Our research shows that technological advancement investments add more to rising load capacity factor than either renewable energy or digitalization. Fourth, our research findings offer crucial insights for policymakers in G8 nations seeking to enhance environmental quality. The findings obtained from the panel cointegration test indicate that the variables exhibit cointegration. The results obtained from the cross sectional autoregressive distributed lag (CS-ARDL) estimators indicate that the short and long-term LCF is positively affected by renewable energy consumption, technological advancement spending, and financial development. Fifth, to achieve this objective, governments may implement various energy policies, including renewable portfolio standards, clean energy subsidies, and tax exemptions for corporations that boost their renewable energy investment and consumption. So it is recommended that the G8 nations consider expanding their technological advancement and digitalization equipment. Spending on TEC (technological advancement) by the G8 nations should prioritize innovations that improve energy efficiency, lower the price of energy, and make it easier to switch to renewable sources. Putting more money towards technological advancement initiatives is another way to assist the environment. In addition, technological advancement incentives can promote load capacity factor enhancement via less polluting industrial production methods, improved refuse recycling technology, and transportation activities that do not rely on fossil fuels. Finally, the novel hypothesis proposed in this work invites new lines of inquiry. In the future, researchers may choose to examine different sets of nations. Human capital, environmental taxes, financial development, and international trade are all potential load capacity factors that could be fascinating to investigate. Findings from recent studies will help us create a more thorough plan for ecological progress.

Data availability
All data generated or analyzed during this study are included in this published article.